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Applications of Action Languages in Cognitive Robotics

  • Esra Erdem
  • Volkan Patoglu
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7265)

Abstract

We summarize some applications of action languages in robotics, focusing on the following three challenges: 1) bridging the gap between low-level continuous geometric reasoning and high-level discrete causal reasoning; 2) embedding background/commonsense knowledge in high-level reasoning; 3) planning/prediction with complex (temporal) goals/constraints. We discuss how these challenges can be handled using computational methods of action languages, and elaborate on the usefulness of action languages to extend the classical 3-layer robot control architecture.

Keywords

Logic Program Motion Planning Action Language Geometric Reasoning Domain Description 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Aker, E., Erdogan, A., Erdem, E., Patoglu, V.: Causal Reasoning for Planning and Coordination of Multiple Housekeeping Robots. In: Delgrande, J.P., Faber, W. (eds.) LPNMR 2011. LNCS, vol. 6645, pp. 311–316. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Aker, E., Erdogan, A., Erdem, E., Patoglu, V.: Housekeeping with multiple autonomous robots: Representation, reasoning and execution. In: Proc. of Commonsense (2011)Google Scholar
  3. 3.
    Akman, V., Erdogan, S.T., Lee, J., Lifschitz, V., Turner, H.: Representing the zoo world and the traffic world in the language of the causal calculator. Artificial Intelligence 153(1-2), 105–140 (2004)CrossRefzbMATHGoogle Scholar
  4. 4.
    Baral, C., Chancellor, K., Tran, N., Tran, N.: Representing and reasoning about signal networks: an illustration using nfkappab dependent signaling pathways. In: Proc. of CSB, pp. 623–628 (2003)Google Scholar
  5. 5.
    Baral, C., Gelfond, M.: Representing concurrent actions in extended logic programming. In: Proc. of IJCAI, pp. 866–873 (1993)Google Scholar
  6. 6.
    Baral, C., Gelfond, M.: Reasoning agents in dynamic domains, pp. 257–279. Kluwer Academic Publishers (2000)Google Scholar
  7. 7.
    Baral, C., Gelfond, M.: Reasoning about intended actions. In: Proc. of AAAI, pp. 689–694 (2005)Google Scholar
  8. 8.
    Baral, C., Gelfond, M., Provetti, A.: Representing actions: Laws, observations and hypotheses. Journal of Logic Programming 31(1-3), 201–243 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Beetz, M., Buss, M., Wollherr, D.: Cognitive Technical Systems — What Is the Role of Artificial Intelligence? In: Hertzberg, J., Beetz, M., Englert, R. (eds.) KI 2007. LNCS (LNAI), vol. 4667, pp. 19–42. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Bornscheuer, S.E., Thielscher, M.: Representing Concurrent Actions and Solving Conflicts. In: Dreschler-Fischer, L., Nebel, B. (eds.) KI 1994. LNCS, vol. 861, pp. 16–27. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  11. 11.
    Bornscheuer, S.E., Thielscher, M.: Representing concurrent actions and solving conflicts. Logic Journal of the IGPL 4(3), 355–368 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Brewka, G., Eiter, T., Truszczynski, M.: Answer set programming at a glance. Communications of ACM 54(12), 92–103 (2011)CrossRefGoogle Scholar
  13. 13.
    Cabalar, P.: Pertinence for Causal Representation of Action Domains. Ph.D. thesis, University of Corunna (2001)Google Scholar
  14. 14.
    Caldiran, O., Haspalamutgil, K., Ok, A., Palaz, C., Erdem, E., Patoglu, V.: Bridging the Gap between High-Level Reasoning and Low-Level Control. In: Erdem, E., Lin, F., Schaub, T. (eds.) LPNMR 2009. LNCS, vol. 5753, pp. 342–354. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Casolary, M., Lee, J.: Representing the language of the causal calculator in answer set programming. In: Proc. of ICLP (Technical Communications), pp. 51–61 (2011)Google Scholar
  16. 16.
    Delgrande, J.P., Schaub, T., Tompits, H.: An extended query language for action languages and its application to aggregates and preferences. In: Proc. of NMR, pp. 362–370 (2006)Google Scholar
  17. 17.
    Doherty, P., Gustafsson, J., Karlsson, L., Kvarnström, J.: Tal: Temporal action logics language specification and tutorial. ETAI 2, 273–306 (1998)MathSciNetGoogle Scholar
  18. 18.
    Dornhege, C., Eyerich, P., Keller, T., Trüg, S., Brenner, M., Nebel, B.: Semantic attachments for domain-independent planning systems. In: Proc. of ICAPS (2009)Google Scholar
  19. 19.
    Dworschak, S., Grell, S., Nikiforova, V.J., Schaub, T., Selbig, J.: Modeling biological networks by action languages via answer set programming. Constraints 13(1-2), 21–65 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Dworschak, S., Grote, T., König, A., Schaub, T., Veber, P.: The system bioc for reasoning about biological models in action language c. In: Proc. of ICTAI (1), pp. 11–18 (2008)Google Scholar
  21. 21.
    Eén, N., Sörensson, N.: An Extensible SAT-solver. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 502–518. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  22. 22.
    Eiter, T., Erdem, E., Fink, M., Senko, J.: Updating action domain descriptions. Artificial Intelligence 174(15), 1172–1221 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Eiter, T., Faber, W., Leone, N., Pfeifer, G., Polleres, A.: A logic programming approach to knowledge-state planning, II: The DLV\(^{\mbox{k}}\) system. Artificial Intelligence 144(1–2), 157–211 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Eiter, T., Leone, N., Mateis, C., Pfeifer, G., Scarcello, F.: A Deductive System for Non-monotonic Reasoning. In: Fuhrbach, U., Dix, J., Nerode, A. (eds.) LPNMR 1997. LNCS, vol. 1265, pp. 363–374. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  25. 25.
    Erdem, E., Haspalamutgil, K., Palaz, C., Patoglu, V., Uras, T.: Combining high-level causal reasoning with low-level geometric reasoning and motion planning for robotic manipulation. In: Proc. of ICRA, pp. 4575–4581 (2011)Google Scholar
  26. 26.
    Erdem, E., Lifschitz, V., Wong, M.D.F.: Wire Routing and Satisfiability Planning. In: Palamidessi, C., Moniz Pereira, L., Lloyd, J.W., Dahl, V., Furbach, U., Kerber, M., Lau, K.-K., Sagiv, Y., Stuckey, P.J. (eds.) CL 2000. LNCS (LNAI), vol. 1861, pp. 822–836. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  27. 27.
    Fox, M., Long, D.: Pddl2.1: An extension to pddl for expressing temporal planning domains. J. Artif. Intell. Res (JAIR) 20, 61–124 (2003)zbMATHGoogle Scholar
  28. 28.
    Gebser, M., Grote, T., Schaub, T.: Coala: A Compiler from Action Languages to ASP. In: Janhunen, T., Niemelä, I. (eds.) JELIA 2010. LNCS, vol. 6341, pp. 360–364. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  29. 29.
    Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Thiele, S.: Engineering an Incremental ASP Solver. In: Garcia de la Banda, M., Pontelli, E. (eds.) ICLP 2008. LNCS, vol. 5366, pp. 190–205. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  30. 30.
    Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T., Schneider, M.T.: Potassco: The potsdam answer set solving collection. AI Communications 24(2), 107–124 (2011)MathSciNetzbMATHGoogle Scholar
  31. 31.
    Gelfond, M., Inclezan, D.: Yet another modular action language. In: Proc. of SEA, pp. 64–78 (2009)Google Scholar
  32. 32.
    Gelfond, M., Lifschitz, V.: Representing actions in extended logic programming. In: Proc. of the Joint International Conference and Symposium on Logic Programming, pp. 559–573 (1992)Google Scholar
  33. 33.
    Gelfond, M., Lifschitz, V.: Representing action and change by logic programs. Journal of Logic Programming 17(2/3&4), 301–321 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Gelfond, M., Lifschitz, V.: Action languages. Electron. Trans. Artif. Intell. 2, 193–210 (1998)MathSciNetGoogle Scholar
  35. 35.
    Giunchiglia, E., Kartha, G.N., Lifschitz, V.: Representing action: Indeterminacy and ramifications. Artificial Intelligence 95(2), 409–438 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N., Turner, H.: Nonmonotonic causal theories. Artificial Intelligence 153(1–2), 49–104 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Giunchiglia, E., Lifschitz, V.: Dependent fluents. In: Proc. of IJCAI, pp. 1964–1969 (1995)Google Scholar
  38. 38.
    Giunchiglia, E., Lifschitz, V.: An action language based on causal explanation: Preliminary report. In: Proc. of AAAI/IAAI, pp. 623–630 (1998)Google Scholar
  39. 39.
    Gravot, F., Cambon, S., Alami, R.: aSyMov:A Planner That Deals with Intricate Symbolic and Geometric Problems. In: Robotics Research the Eleventh International Symposium. Springer Tracts in Advanced Robotics, vol. 15, pp. 100–110. Springer (2005)Google Scholar
  40. 40.
    Haspalamutgil, K.: Multi-Robot Systems in Cognitive Factories: Representation, Reasoning, Execution and Monitoring. Master’s thesis, Sabanci University, Istanbul, Turkey (2011)Google Scholar
  41. 41.
    Hauser, K., Latombe, J.C.: Integrating task and PRM motion planning: Dealing with many infeasible motion planning queries. In: Workshop on Bridging the Gap between Task and Motion Planning at ICAPS (2009)Google Scholar
  42. 42.
    Hoffmann, J., Nebel, B.: The ff planning system: Fast plan generation through heuristic search. J. Artif. Intell. Res (JAIR) 14, 253–302 (2001)zbMATHGoogle Scholar
  43. 43.
    Hopton, L., Cliffe, O., De Vos, M., Padget, J.: AQL: A Query Language for Action Domains Modelled Using Answer Set Programming. In: Erdem, E., Lin, F., Schaub, T. (eds.) LPNMR 2009. LNCS, vol. 5753, pp. 437–443. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  44. 44.
    Kaelbling, L.P., Lozano-Perez, T.: Hierarchical planning in the now. In: Proc. of ICRA Workshop on Mobile Manipulation (2010)Google Scholar
  45. 45.
    Kartha, G.N., Lifschitz, V.: Actions with indirect effects (preliminary report). In: Proc. of KR, pp. 341–350 (1994)Google Scholar
  46. 46.
    Kowalski, R., Sergot, M.: A logic-based calculus of events. New Gen. Comput. 4(1), 67–95 (1986)CrossRefGoogle Scholar
  47. 47.
    Latombe, J.C.: Robot Motion Planning. Kluwer Academic, Dordrecht (1991)CrossRefzbMATHGoogle Scholar
  48. 48.
    Levesque, H., Lakemeyer, G.: Cognitive robotics. In: Handbook of Knowledge Representation. Elsevier (2007)Google Scholar
  49. 49.
    Levesque, H.J., Pirri, F., Reiter, R.: Foundations for the situation calculus. ETAI 2, 159–178 (1998)Google Scholar
  50. 50.
    Lifschitz, V.: Two components of an action language. Annals of Mathematics in Artificial Intelligence 21(2–4), 305–320 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  51. 51.
    Lifschitz, V.: Action languages, answer sets and planning. In: The Logic Programming Paradigm: a 25-Year Perspective, pp. 357–373. Springer (1999)Google Scholar
  52. 52.
    Lifschitz, V.: What is answer set programming? In: Proc. of. AAAI, pp. 1594–1597 (2008)Google Scholar
  53. 53.
    Lifschitz, V., Ren, W.: A modular action description language. In: Proc. of AAAI (2006)Google Scholar
  54. 54.
    Lifschitz, V., Turner, H.: Representing Transition Systems by Logic Programs. In: Gelfond, M., Leone, N., Pfeifer, G. (eds.) LPNMR 1999. LNCS (LNAI), vol. 1730, pp. 92–106. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  55. 55.
    Liu, H., Singh, P.: ConceptNet: A practical commonsense reasoning toolkit. BT Technology Journal 22 (2004)Google Scholar
  56. 56.
    Marek, V., Truszczyński, M.: Stable models and an alternative logic programming paradigm. In: The Logic Programming Paradigm: a 25-Year Perspective, pp. 375–398. Springer (1999)Google Scholar
  57. 57.
    McCain, N.: Causality in Commonsense Reasoning about Actions. Ph.D. thesis, University of Texas at Austin (1997)Google Scholar
  58. 58.
    McCain, N., Turner, H.: A causal theory of ramifications and qualifications. In: Proc. of IJCAI, pp. 1978–1984 (1995)Google Scholar
  59. 59.
    McCain, N., Turner, H.: Causal theories of action and change. In: Proc. of AAAI/IAAI, pp. 460–465 (1997)Google Scholar
  60. 60.
    McCarthy, J.: Situations, actions, and causal laws. Tech. rep., Stanford University (1963)Google Scholar
  61. 61.
    Miller, R., Shanahan, M.: The event calculus in classical logic - alternative axiomatisations. ETAI 3(A), 77–105 (1999)MathSciNetGoogle Scholar
  62. 62.
    Niemelä, I.: Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25, 241–273 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  63. 63.
    Plaku, E., Hager, G.D.: Sampling-based motion and symbolic action planning with geometric and differential constraints. In: Proc. of ICRA, pp. 5002–5008 (2010)Google Scholar
  64. 64.
    Sandewall, E.: Features and Fluents: A Systematic Approach to the Representation of Knowledge about Dynamical Systems. Oxford University Press (1994)Google Scholar
  65. 65.
    Sandewall, E.: Cognitive robotics logic and its metatheory: Features and fluents revisited. ETAI 2, 307–329 (1998)MathSciNetGoogle Scholar
  66. 66.
    Son, T.C., Baral, C.: Formalizing sensing actions a transition function based approach. Artificial Intelligence 125(1–2), 19–91 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  67. 67.
    Thielscher, M.: Introduction to the fluent calculus. ETAI 2, 179–192 (1998)MathSciNetGoogle Scholar
  68. 68.
    Tran, N., Baral, C.: Reasoning about non-immediate triggers in biological networks. Ann. Math. Artif. Intell. 51(2–4), 267–293 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  69. 69.
    Tran, N., Baral, C.: Hypothesizing about signaling networks. J. Applied Logic 7(3), 253–274 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  70. 70.
    Tran, N., Baral, C., Nagaraj, V.J., Joshi, L.: Knowledge-Based Integrative Framework for Hypothesis Formation in Biochemical Networks. In: Ludäscher, B., Raschid, L. (eds.) DILS 2005. LNCS (LNBI), vol. 3615, pp. 121–136. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  71. 71.
    Tran, N., Baral, C., Shankland, C.: Issues in reasoning about interaction networks in cells: Necessity of event ordering knowledge. In: Proc. of AAAI, pp. 676–681 (2005)Google Scholar
  72. 72.
    Turner, H.: Representing actions in logic programs and default theories: A situation calculus approach. Journal of Logic Programming 31(1–3), 245–298 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  73. 73.
    Uras, T.: Applications of AI Planning in Genome Rearrangement and in Multi-Robot Systems. Master’s thesis, Sabanci University, Istanbul, Turkey (2011)Google Scholar
  74. 74.
    Wolfe, J., Marthi, B., Russell, S.: Combined task and motion planning for mobile manipulation. In: International Conference on Automated Planning and Scheduling (2010)Google Scholar
  75. 75.
    Zaeh, M., Beetz, M., Shea, K., Reinhart, G., Bender, K., Lau, C., Ostgathe, M., Vogl, W., Wiesbeck, M., Engelhard, M., Ertelt, C., Rühr, T., Friedrich, M., Herle, S.: The cognitive factory. In: Changeable and Reconf. Manufacturing Systems, pp. 355–371 (2009)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Esra Erdem
    • 1
  • Volkan Patoglu
    • 1
  1. 1.Faculty of Engineering and Natural SciencesSabancı UniversityTuzlaTurkey

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